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Super Payments profile image Clément Raul
Clément Raul for Super Payments

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📈 Period-over-Period Measures in Looker: A Simpler, Better Way to Analyze Time Trends

#data #analytics #looker

Tracking change over time—month-over-month, year-over-year—is essential for monitoring performance. Until recently, doing this in Looker meant relying on table calculations, which could be:

  • Hard to read and maintain
  • Prone to human error
  • Limited to visualization-only logic

Looker's new period-over-period measures help bring time-based comparisons into LookML itself - making it easier to build reliable, reusable metrics. (https://cloud.google.com/looker/docs/period-over-period)

đŸ§± Before: Table Calculations

Previously, getting last year’s value for a metric required a workaround like the following table calculation:
offset(${payment_transactions_fact.count_successful_transactions}, 12)

Limitations we encountered with this approach:

  • ⚠ Only works in visualizations—can’t be filtered or reused elsewhere
  • đŸ§© Logic cannot get duplicated across dashboards
  • 🔍 Harder to QA and understand over time
  • 📉 Fragile if the time grain or sort order changes

✅ Now: LookML Period-over-Period Measures

With the period_over_period measure type, we can now move this logic into our LookML layer.

Example: Last Year’s Value

measure: count_successful_transactions_last_year {
  type: period_over_period
  description: "Successful transactions from the previous year"
  based_on: count_successful_transactions
  based_on_time: created_year
  period: year
  kind: previous
}
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Example: Year-over-Year % Change

measure: count_successful_transactions_last_year_relative_change {
  type: period_over_period
  description: "Year-over-year % change in successful transactions"
  based_on: count_successful_transactions
  based_on_time: created_year
  period: year
  kind: relative_change
  value_format_name: percent_0
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Example: Looker Explore
Looker table

Example: Looker Visualisation
Looker visualisation

🏱 How We Use It

At Super, we’ve started using these measures to track key metrics like:

  • 📩 Sales – Year-over-year trends
  • đŸ§Č Customer and merchant retention – Month-over-month comparisons
  • 🚀 Feature impact – Week-over-week shifts after a product launch

Because these are built into our LookML layer, they can be reused across different dashboards, which helps ensure consistency and saves time.

🔍 Why This Helps

  • ✅ Cleaner and centralized logic – Easier to review and test
  • 📊 Works across dashboards – Filterable, sortable, and visualization-friendly
  • 🔧 Adaptable – Supports different time periods (week, month, quarter, year)

💡 Final Thoughts

This feature might seem small, but it's been a noticeable improvement for our data team. It reduces repetitive work, helps standardise time-based comparisons, and makes dashboards easier to maintain and trust.

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